{"url":"/method/spatial-pyramid-pooling","slug":"spatial-pyramid-pooling","name":"Spatial Pyramid Pooling","full_name":"Spatial Pyramid Pooling","full_name_withheld":false,"description_markdown":"** Spatial Pyramid Pooling (SPP)** is a pooling layer that removes the fixed-size constraint of the network, i.e. a CNN does not require a fixed-size input image. Specifically, we add an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are then fed into the fully-connected layers (or other classifiers). In other words, we perform some information aggregation at a deeper stage of the network hierarchy (between convolutional layers and fully-connected layers) to avoid the need for cropping or warping at the beginning.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","paper":"/paper/spatial-pyramid-pooling-in-deep-convolutional","first_author":"Kaiming He","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/spatial-pyramid-pooling-in-deep-convolutional"},"source":{"url":"http://arxiv.org/abs/1406.4729v4","title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/yueruchen/sppnet-pytorch/blob/270529337baa5211538bf553bda222b9140838b3/spp_layer.py#L2","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Pooling Operations","url":"/methods/category/pooling-operations","pwc_aliases":["pooling-operation"]}],"n_papers_tagged":285,"archive_num_papers":285,"papers_newest_first":[{"paper":null,"title":"Pattern-Based Phase-Separation of Tracer and Dispersed Phase Particles in Two-Phase Defocusing Particle Tracking Velocimetry","date":"2025-06-22","arxiv_id":"2506.18157","n_code_links":0,"syntology":null},{"paper":null,"title":"DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View","date":"2025-06-10","arxiv_id":"2506.08534","n_code_links":0,"syntology":null},{"paper":null,"title":"SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling","date":"2025-04-17","arxiv_id":"2504.13310","n_code_links":0,"syntology":null},{"paper":null,"title":"Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing","date":"2025-04-03","arxiv_id":"2504.02335","n_code_links":0,"syntology":null},{"paper":null,"title":"DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset","date":"2025-03-27","arxiv_id":"2503.21323","n_code_links":0,"syntology":null},{"paper":"/paper/event-based-crossing-dataset-ebcd","title":"Event-Based Crossing Dataset (EBCD)","date":"2025-03-21","arxiv_id":"2503.17499","n_code_links":1,"syntology":null},{"paper":"/paper/walnutdata-a-uav-remote-sensing-dataset-of","title":"WalnutData: A UAV Remote Sensing Dataset of Green Walnuts and Model Evaluation","date":"2025-02-27","arxiv_id":"2502.20092","n_code_links":1,"syntology":null},{"paper":null,"title":"DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images","date":"2025-02-13","arxiv_id":"2502.09256","n_code_links":0,"syntology":null},{"paper":null,"title":"Vision-Integrated LLMs for Autonomous Driving Assistance : Human Performance Comparison and Trust Evaluation","date":"2025-02-06","arxiv_id":"2502.06843","n_code_links":0,"syntology":null},{"paper":null,"title":"YOLOv4: A Breakthrough in Real-Time Object Detection","date":"2025-02-06","arxiv_id":"2502.04161","n_code_links":0,"syntology":null},{"paper":null,"title":"SPFFNet: Strip Perception and Feature Fusion Spatial Pyramid Pooling for Fabric Defect Detection","date":"2025-02-03","arxiv_id":"2502.01445","n_code_links":0,"syntology":null},{"paper":null,"title":"Efficient Object Detection of Marine Debris using Pruned YOLO Model","date":"2025-01-27","arxiv_id":"2501.16571","n_code_links":0,"syntology":null},{"paper":null,"title":"Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumour Segmentation","date":"2025-01-22","arxiv_id":"2501.13129","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-brain-tumor-segmentation-using","title":"Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning","date":"2025-01-19","arxiv_id":"2501.11196","n_code_links":1,"syntology":null},{"paper":"/paper/ai-driven-water-segmentation-with-deep","title":"AI Driven Water Segmentation with deep learning models for Enhanced Flood Monitoring","date":"2025-01-14","arxiv_id":"2501.08266","n_code_links":1,"syntology":null},{"paper":null,"title":"MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy","date":"2024-12-27","arxiv_id":"2412.19464","n_code_links":0,"syntology":null},{"paper":null,"title":"Object Detection Approaches to Identifying Hand Images with High Forensic Values","date":"2024-12-21","arxiv_id":"2412.16431","n_code_links":0,"syntology":null},{"paper":null,"title":"Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)","date":"2024-12-19","arxiv_id":"2412.15347","n_code_links":0,"syntology":null},{"paper":"/paper/a4-unet-deformable-multi-scale-attention","title":"A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation","date":"2024-12-08","arxiv_id":"2412.06088","n_code_links":1,"syntology":null},{"paper":"/paper/mapping-africa-settlements-high-resolution","title":"Mapping Africa Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery","date":"2024-11-05","arxiv_id":"2411.02935","n_code_links":1,"syntology":null},{"paper":"/paper/cascrnet-an-atrous-spatial-pyramid-pooling","title":"CASCRNet: An Atrous Spatial Pyramid Pooling and Shared Channel Residual based Network for Capsule Endoscopy","date":"2024-10-23","arxiv_id":"2410.17863","n_code_links":2,"syntology":null},{"paper":"/paper/attention-guided-residual-u-net-with-se","title":"Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images","date":"2024-10-18","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"title":"Automated Road Extraction from Satellite Imagery Integrating Dense Depthwise Dilated Separable Spatial Pyramid Pooling with DeepLabV3+","date":"2024-10-18","arxiv_id":"2410.14836","n_code_links":0,"syntology":null},{"paper":null,"title":"Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom","date":"2024-09-25","arxiv_id":"2409.17025","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-open-source-ultrasound-dataset-with","title":"A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation","date":"2024-09-24","arxiv_id":"2409.16441","n_code_links":1,"syntology":null},{"paper":null,"title":"UICE-MIRNet guided image enhancement for underwater object detection","date":"2024-09-24","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism","date":"2024-09-13","arxiv_id":"2409.08588","n_code_links":0,"syntology":null},{"paper":null,"title":"WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking","date":"2024-09-05","arxiv_id":"2409.03902","n_code_links":0,"syntology":null},{"paper":"/paper/pyramidmamba-rethinking-pyramid-feature","title":"PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery","date":"2024-06-16","arxiv_id":"2406.10828","n_code_links":2,"syntology":null},{"paper":null,"title":"Cycle-YOLO: A Efficient and Robust Framework for Pavement Damage Detection","date":"2024-05-28","arxiv_id":"2405.17905","n_code_links":0,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":102},{"task":"/task/object-detection","name":"Object Detection","papers":86},{"task":"/task/object-detection-1","name":"object-detection","papers":79},{"task":"/task/segmentation","name":"Segmentation","papers":75},{"task":"/task/object","name":"Object","papers":41},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":37},{"task":"/task/decoder","name":"Decoder","papers":27},{"task":"/task/image-classification","name":"Image Classification","papers":19},{"task":"/task/image-classification","name":"image-classification","papers":17},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":14},{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":13},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":13},{"task":null,"name":"GPU","papers":13},{"task":"/task/medical-image-segmentation","name":"Medical Image Segmentation","papers":13},{"task":"/task/real-time-object-detection","name":"Real-Time Object Detection","papers":13},{"task":"/task/deep-learning","name":"Deep Learning","papers":12},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":11},{"task":"/task/classification-1","name":"Classification","papers":10},{"task":"/task/classification","name":"General Classification","papers":9},{"task":"/task/domain-adaptation","name":"Domain Adaptation","papers":8}],"tasks_shown":20,"n_tasks":262,"usage_by_year":[{"year":"2014","papers":3},{"year":"2015","papers":1},{"year":"2016","papers":4},{"year":"2017","papers":1},{"year":"2018","papers":14},{"year":"2019","papers":27},{"year":"2020","papers":43},{"year":"2021","papers":49},{"year":"2022","papers":57},{"year":"2023","papers":46},{"year":"2024","papers":25},{"year":"2025","papers":15}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/spatial-pyramid-pooling"},"syntology_read_at":"2026-09-25T09:33:49+00:00"}